The energy industry is undergoing a massive revolution. As customer expectations rise, energy providers must determine the best ways to balance supply and demand while ensuring reliable transmission. Machine learning can help them stay on top of predictive maintenance to maximize uptime at generation plants, find problem areas on distribution lines using overhead images, and spot usage trends using smart meter data. Discover how Intel AI will power this future.
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Devon Energy Podcast

Devon Energy Increases Efficiency and Safety with AI

Kathy Ball, Manager E&P Analytics and Data Science for Devon Energy, joins us live from Intel AI Day in San Francisco. A data science pioneer in the oil & gas industry, Devon Energy uses AI to find oil and gas more effectively, efficiently and safely. In this interview, Ball assesses the use of AI technologies within Devon and the industry at large and reports on business outcomes Devon is realizing via AI. For more information on Devon Energy’s use of AI, please visit and follow Ball on Twitter at

Industry Efficiencies

Industry Efficiencies Delivered by Intel AI

Early detection of tumors. Predicting equipment failures before they happen. Having a natural conversation with a smart, digital assistant. Making retail more personal than ever. AI is impacting our lives in exciting ways every day. This is AI powered by Intel, and companies around the globe are using it to make money, save money, and advance the future of their industry. AI is helping to produce better products with less work, identify maintenance problems before they grow more costly, and apply proactive solutions before predicted issues grow more serious. Ultimately, AI is helping us do more, better, and more easily, increasing efficiency.

Power System Infrastructure Monitoring Using Deep Learning on Intel® Architecture

Power System Infrastructure Monitoring Using Deep Learning on Intel® Architecture

The work in this paper evaluates the performance of Intel® Xeon® processor powered machines for running deep learning on the GoogleNet* topology (Inception* v3). The functional problem tackled is the identification of power system components such as pylons, conductors, and insulators from the real-world video footage captured by unmanned aerial vehicles (UAVs) or commercially available drones…

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